The pooling layer, also known as the sub-sampling or down-sampling layer, takes a certain input volume and reduces it to a single value as shown in Figure REF . This provides invariance to small distortions in the data. The pooling layer helps the model to control overfitting as the size of data and model parameters both are reduced which also leads to a decrease in the computational time. The commonly used down-sampling operations are max-pooling, average-pooling, and sum-pooling. Recently, a pooling technique, wavelet-pooling is introduced in [1]} whose performance is commensurable to max-pooling and average-pooling. Alternatively, [2]} proposed another trend in which the pooling layer is replaced by the CONV layer of increased filter stride.
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